The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 20, 2026

Filed:

Aug. 26, 2022
Applicant:

The Trustees of Princeton University, Princeton, NJ (US);

Inventors:

Hsuan-Tung Peng, Princeton, NJ (US);

Joshua Lederman, Princeton, NJ (US);

Lei Xu, Priinceton Junction, NJ (US);

Thomas Ferreira De Lima, Princeton, NJ (US);

Chaoran Huang, Hong Kong, CN;

David Rosenbluth, Swarthmore, PA (US);

Paul Prucnal, Princeton, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04B 17/309 (2015.01); H04B 1/00 (2006.01); H04B 17/391 (2015.01);
U.S. Cl.
CPC ...
H04B 17/309 (2015.01); H04B 1/0007 (2013.01); H04B 17/3913 (2015.01);
Abstract

A system and method may be provided for real-time RF fingerprinting, that includes obtaining residual data from each transmission via residual data preprocessing, processing at least a portion of the residual data using a photonic hardware-compatible continuous-time recurrent neural network (CTRNN) model to correlate temporal information and generate informative features, classifying the residual data based on the informative features using a convolutional neural network (CNN) model, and outputting a prediction of which of the plurality of devices at least one of the plurality of adjacent data units was transmitted from. The CNN model may be configured to convolute the generated informative features using at least one convolution-1D layer, for each of which it may select a maximum value for every two consecutive sequential points output from the convolution-1D layer using a max-pooling layer, and flatten and fully connect the output from a last max-pooling layer using a fully-connected layer.


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